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This paper discusses the problem of learning language from unprocessed text and speech signals, concentrating on the problem of learning a lexicon. In particular, it argues for a representation of language in which linguistic parameters like words are built by perturbing a composition of existing parameters. The power of the representation is demonstrated by several examples in text segmentation and compression, acquisition of a lexicon from raw speech, and the acquisition of mappings betweendoi:10.3115/981863.981907 dblp:conf/acl/Marcken96 fatcat:44cpfeneijewlhikdy3lp7ejki